Overview

Dataset statistics

Number of variables14
Number of observations6497
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory710.7 KiB
Average record size in memory112.0 B

Variable types

Numeric13
Categorical1

Alerts

Unnamed: 0 is highly overall correlated with is red or white wineHigh correlation
fixed acidity is highly overall correlated with is red or white wineHigh correlation
volatile acidity is highly overall correlated with is red or white wineHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
is red or white wine is highly overall correlated with Unnamed: 0 and 4 other fieldsHigh correlation
citric acid has 151 (2.3%) zerosZeros

Reproduction

Analysis started2023-11-21 16:50:40.359103
Analysis finished2023-11-21 16:50:54.332511
Duration13.97 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION 

Distinct4898
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2042.5356
Minimum0
Maximum4897
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:54.405765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162
Q1812
median1649
Q33273
95-th percentile4572.2
Maximum4897
Range4897
Interquartile range (IQR)2461

Descriptive statistics

Standard deviation1436.9264
Coefficient of variation (CV)0.70350126
Kurtosis-1.1158853
Mean2042.5356
Median Absolute Deviation (MAD)1100
Skewness0.41041906
Sum13270354
Variance2064757.5
MonotonicityNot monotonic
2023-11-21T16:50:54.512184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
< 0.1%
1063 2
 
< 0.1%
1073 2
 
< 0.1%
1072 2
 
< 0.1%
1071 2
 
< 0.1%
1070 2
 
< 0.1%
1069 2
 
< 0.1%
1068 2
 
< 0.1%
1067 2
 
< 0.1%
1066 2
 
< 0.1%
Other values (4888) 6477
99.7%
ValueCountFrequency (%)
0 2
< 0.1%
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
ValueCountFrequency (%)
4897 1
< 0.1%
4896 1
< 0.1%
4895 1
< 0.1%
4894 1
< 0.1%
4893 1
< 0.1%
4892 1
< 0.1%
4891 1
< 0.1%
4890 1
< 0.1%
4889 1
< 0.1%
4888 1
< 0.1%

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2153071
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:54.611934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2964338
Coefficient of variation (CV)0.17967825
Kurtosis5.0611607
Mean7.2153071
Median Absolute Deviation (MAD)0.6
Skewness1.7232896
Sum46877.85
Variance1.6807405
MonotonicityNot monotonic
2023-11-21T16:50:54.714866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 354
 
5.4%
6.6 327
 
5.0%
6.4 305
 
4.7%
7 282
 
4.3%
6.9 279
 
4.3%
7.2 273
 
4.2%
6.7 264
 
4.1%
7.1 257
 
4.0%
6.5 242
 
3.7%
7.4 238
 
3.7%
Other values (96) 3676
56.6%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
< 0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.1%
4.9 8
 
0.1%
5 30
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 2
< 0.1%
15 2
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct187
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.339666
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:54.813489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.16463647
Coefficient of variation (CV)0.48470107
Kurtosis2.8253724
Mean0.339666
Median Absolute Deviation (MAD)0.08
Skewness1.4950965
Sum2206.81
Variance0.027105169
MonotonicityNot monotonic
2023-11-21T16:50:54.917941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 286
 
4.4%
0.24 266
 
4.1%
0.26 256
 
3.9%
0.25 238
 
3.7%
0.22 235
 
3.6%
0.27 232
 
3.6%
0.23 221
 
3.4%
0.2 217
 
3.3%
0.3 214
 
3.3%
0.32 205
 
3.2%
Other values (177) 4127
63.5%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 6
 
0.1%
0.11 13
 
0.2%
0.115 3
 
< 0.1%
0.12 37
0.6%
0.125 3
 
< 0.1%
0.13 44
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

ZEROS 

Distinct89
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31863322
Minimum0
Maximum1.66
Zeros151
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:55.020619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.14531786
Coefficient of variation (CV)0.45606628
Kurtosis2.3972392
Mean0.31863322
Median Absolute Deviation (MAD)0.07
Skewness0.47173067
Sum2070.16
Variance0.021117282
MonotonicityNot monotonic
2023-11-21T16:50:55.139677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 337
 
5.2%
0.28 301
 
4.6%
0.32 289
 
4.4%
0.49 283
 
4.4%
0.26 257
 
4.0%
0.34 249
 
3.8%
0.29 244
 
3.8%
0.27 236
 
3.6%
0.24 232
 
3.6%
0.31 230
 
3.5%
Other values (79) 3839
59.1%
ValueCountFrequency (%)
0 151
2.3%
0.01 40
 
0.6%
0.02 56
 
0.9%
0.03 32
 
0.5%
0.04 41
 
0.6%
0.05 25
 
0.4%
0.06 30
 
0.5%
0.07 34
 
0.5%
0.08 37
 
0.6%
0.09 42
 
0.6%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 2
 
< 0.1%

residual sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct316
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4432353
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:55.260403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7578037
Coefficient of variation (CV)0.87407644
Kurtosis4.3592719
Mean5.4432353
Median Absolute Deviation (MAD)1.7
Skewness1.4354043
Sum35364.7
Variance22.636696
MonotonicityNot monotonic
2023-11-21T16:50:55.464098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 235
 
3.6%
1.8 228
 
3.5%
1.6 223
 
3.4%
1.4 219
 
3.4%
1.2 195
 
3.0%
2.2 187
 
2.9%
2.1 179
 
2.8%
1.9 176
 
2.7%
1.7 175
 
2.7%
1.5 172
 
2.6%
Other values (306) 4508
69.4%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.4%
0.9 41
 
0.6%
0.95 4
 
0.1%
1 93
1.4%
1.05 1
 
< 0.1%
1.1 146
2.2%
1.15 3
 
< 0.1%
1.2 195
3.0%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct214
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056033862
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:55.575273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.035033601
Coefficient of variation (CV)0.62522197
Kurtosis50.898051
Mean0.056033862
Median Absolute Deviation (MAD)0.011
Skewness5.3998277
Sum364.052
Variance0.0012273532
MonotonicityNot monotonic
2023-11-21T16:50:55.679471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 206
 
3.2%
0.036 200
 
3.1%
0.042 187
 
2.9%
0.046 185
 
2.8%
0.05 182
 
2.8%
0.04 182
 
2.8%
0.048 182
 
2.8%
0.047 175
 
2.7%
0.045 174
 
2.7%
0.038 169
 
2.6%
Other values (204) 4655
71.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 10
0.2%
0.019 9
0.1%
0.02 16
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
< 0.1%
0.414 2
< 0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.525319
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:55.793019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.7494
Coefficient of variation (CV)0.58146483
Kurtosis7.9062381
Mean30.525319
Median Absolute Deviation (MAD)12
Skewness1.2200661
Sum198323
Variance315.04119
MonotonicityNot monotonic
2023-11-21T16:50:55.894415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 183
 
2.8%
6 170
 
2.6%
26 161
 
2.5%
15 157
 
2.4%
24 152
 
2.3%
31 152
 
2.3%
17 149
 
2.3%
34 146
 
2.2%
35 144
 
2.2%
23 142
 
2.2%
Other values (125) 4941
76.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.9%
4 52
 
0.8%
5 129
2.0%
5.5 1
 
< 0.1%
6 170
2.6%
7 96
1.5%
8 91
1.4%
9 91
1.4%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.74457
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:55.995095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.521855
Coefficient of variation (CV)0.48833265
Kurtosis-0.37166365
Mean115.74457
Median Absolute Deviation (MAD)39
Skewness-0.0011774782
Sum751992.5
Variance3194.72
MonotonicityNot monotonic
2023-11-21T16:50:56.089674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 72
 
1.1%
113 65
 
1.0%
117 57
 
0.9%
122 57
 
0.9%
128 56
 
0.9%
98 56
 
0.9%
124 56
 
0.9%
114 56
 
0.9%
118 55
 
0.8%
150 54
 
0.8%
Other values (266) 5913
91.0%
ValueCountFrequency (%)
6 3
 
< 0.1%
7 4
 
0.1%
8 14
 
0.2%
9 15
0.2%
10 28
0.4%
11 26
0.4%
12 29
0.4%
13 28
0.4%
14 33
0.5%
15 35
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99469663
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:56.185198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673
Coefficient of variation (CV)0.0030146609
Kurtosis6.606067
Mean0.99469663
Median Absolute Deviation (MAD)0.00231
Skewness0.50360173
Sum6462.544
Variance8.9920398 × 10-6
MonotonicityNot monotonic
2023-11-21T16:50:56.287845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9976 69
 
1.1%
0.9972 69
 
1.1%
0.998 64
 
1.0%
0.992 64
 
1.0%
0.9928 63
 
1.0%
0.9986 61
 
0.9%
0.9962 59
 
0.9%
0.9966 59
 
0.9%
0.9956 55
 
0.8%
0.9968 55
 
0.8%
Other values (988) 5879
90.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
 
< 0.1%
1.0103 2
< 0.1%
1.00369 2
< 0.1%
1.0032 1
 
< 0.1%
1.00315 3
< 0.1%
1.00295 2
< 0.1%
1.00289 1
 
< 0.1%
1.0026 2
< 0.1%
1.00242 2
< 0.1%
1.00241 1
 
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2185008
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:56.378777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607872
Coefficient of variation (CV)0.049957173
Kurtosis0.36765727
Mean3.2185008
Median Absolute Deviation (MAD)0.11
Skewness0.3868388
Sum20910.6
Variance0.025852524
MonotonicityNot monotonic
2023-11-21T16:50:56.481317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 200
 
3.1%
3.14 193
 
3.0%
3.22 185
 
2.8%
3.2 176
 
2.7%
3.15 170
 
2.6%
3.19 170
 
2.6%
3.18 168
 
2.6%
3.24 161
 
2.5%
3.1 154
 
2.4%
3.12 154
 
2.4%
Other values (98) 4766
73.4%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
 
< 0.1%
2.8 3
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.84 1
 
< 0.1%
2.85 9
0.1%
2.86 10
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53126828
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:56.578760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14880587
Coefficient of variation (CV)0.28009554
Kurtosis8.6536988
Mean0.53126828
Median Absolute Deviation (MAD)0.08
Skewness1.79727
Sum3451.65
Variance0.022143188
MonotonicityNot monotonic
2023-11-21T16:50:56.678325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 276
 
4.2%
0.46 243
 
3.7%
0.54 235
 
3.6%
0.44 232
 
3.6%
0.38 214
 
3.3%
0.48 208
 
3.2%
0.52 203
 
3.1%
0.49 197
 
3.0%
0.47 191
 
2.9%
0.45 190
 
2.9%
Other values (101) 4308
66.3%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 4
 
0.1%
0.27 13
 
0.2%
0.28 13
 
0.2%
0.29 16
 
0.2%
0.3 31
0.5%
0.31 35
0.5%
0.32 54
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
< 0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.491801
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:56.775493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1927117
Coefficient of variation (CV)0.11368037
Kurtosis-0.53168738
Mean10.491801
Median Absolute Deviation (MAD)0.9
Skewness0.56571773
Sum68165.23
Variance1.4225613
MonotonicityNot monotonic
2023-11-21T16:50:56.875666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 367
 
5.6%
9.4 332
 
5.1%
9.2 271
 
4.2%
10 229
 
3.5%
10.5 227
 
3.5%
11 217
 
3.3%
9 215
 
3.3%
9.8 214
 
3.3%
10.4 194
 
3.0%
9.3 193
 
3.0%
Other values (101) 4038
62.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 5
 
0.1%
8.5 10
 
0.2%
8.6 23
 
0.4%
8.7 80
 
1.2%
8.8 109
1.7%
8.9 95
1.5%
9 215
3.3%
9.05 1
 
< 0.1%
9.1 167
2.6%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 12
0.2%
13.9 3
 
< 0.1%
13.8 2
 
< 0.1%
13.7 7
0.1%
13.6 13
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8183777
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-11-21T16:50:56.952124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87325527
Coefficient of variation (CV)0.1500857
Kurtosis0.23232227
Mean5.8183777
Median Absolute Deviation (MAD)1
Skewness0.18962269
Sum37802
Variance0.76257477
MonotonicityNot monotonic
2023-11-21T16:50:57.041905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2836
43.7%
5 2138
32.9%
7 1079
 
16.6%
4 216
 
3.3%
8 193
 
3.0%
3 30
 
0.5%
9 5
 
0.1%
ValueCountFrequency (%)
3 30
 
0.5%
4 216
 
3.3%
5 2138
32.9%
6 2836
43.7%
7 1079
 
16.6%
8 193
 
3.0%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 193
 
3.0%
7 1079
 
16.6%
6 2836
43.7%
5 2138
32.9%
4 216
 
3.3%
3 30
 
0.5%

is red or white wine
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
0
4898 
1
1599 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6497
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4898
75.4%
1 1599
 
24.6%

Length

2023-11-21T16:50:57.121100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-21T16:50:57.189322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4898
75.4%
1 1599
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 4898
75.4%
1 1599
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6497
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4898
75.4%
1 1599
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 6497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4898
75.4%
1 1599
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4898
75.4%
1 1599
 
24.6%

Interactions

2023-11-21T16:50:53.046025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.156788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.068003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.993155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.032394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.974530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.941770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.000177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.113443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.095622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.066050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.123405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.097366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.116391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.220941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.136723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.061502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.104355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.046919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.018805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.074149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.199573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.160324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.139633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.201745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.169218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.184065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.293642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.206255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.138112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.179364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.122638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.091961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.153170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.279627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.236390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.216304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.273497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.241884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.260536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.367717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.279720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.212856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.253863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.201708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.168205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.240463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.361804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.313431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.369707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.356338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.317400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.425119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.438005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.351748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.283755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.323415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.271805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.241298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.315440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.439406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.388076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.439199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.431186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.390013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.495907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.505746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.430604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.356705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.395377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.342890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.310223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.391300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.506047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.462796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.510417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.507487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.463480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.567487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.575921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.502318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.429278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.465766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.414380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.379015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.473414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.578120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.540106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.586415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.579625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.535267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.635888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.645710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.575431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.502591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.549669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.491455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.457025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.572904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.668760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.616822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.690790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.654383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.608118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.698703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.715936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.639538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.567521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.614491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.561298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.523368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.652065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.734070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.694614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.762597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.723333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.678039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.767481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.781812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.708228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.645819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.684768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.635629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.594465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.734832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.802702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.763413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.836654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.796334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.749180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.835908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.851494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.777805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.724485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.754706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.711459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.668009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.824044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.871408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.835681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.906258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.873488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.821902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.909662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.924527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.852943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.882107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.828077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.788795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.746489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:47.920110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.954247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.911505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:50.978940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.951172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.898835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:53.985676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:41.998468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:42.923833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:43.956893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:44.900460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:45.864314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:46.920702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:48.024829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.027652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:49.989865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:51.049511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.025084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-21T16:50:52.975463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-21T16:50:57.252399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Unnamed: 0fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualityis red or white wine
Unnamed: 01.000-0.385-0.303-0.0390.143-0.3840.2560.245-0.352-0.231-0.2500.1870.1000.552
fixed acidity-0.3851.0000.2000.271-0.0320.356-0.260-0.2330.434-0.2500.220-0.111-0.0980.504
volatile acidity-0.3030.2001.000-0.295-0.0640.416-0.366-0.3440.2610.1950.255-0.024-0.2580.664
citric acid-0.0390.271-0.2951.0000.075-0.0740.1220.1590.066-0.2860.0370.0200.1060.424
residual sugar0.143-0.032-0.0640.0751.000-0.0360.3880.4550.527-0.229-0.138-0.329-0.0170.350
chlorides-0.3840.3560.416-0.074-0.0361.000-0.260-0.2680.5910.1640.370-0.401-0.2950.765
free sulfur dioxide0.256-0.260-0.3660.1220.388-0.2601.0000.7410.006-0.165-0.221-0.1860.0870.419
total sulfur dioxide0.245-0.233-0.3440.1590.455-0.2680.7411.0000.062-0.243-0.257-0.309-0.0550.800
density-0.3520.4340.2610.0660.5270.5910.0060.0621.0000.0120.275-0.699-0.3230.322
pH-0.231-0.2500.195-0.286-0.2290.164-0.165-0.2430.0121.0000.2540.1400.0330.333
sulphates-0.2500.2200.2550.037-0.1380.370-0.221-0.2570.2750.2541.0000.0050.0300.472
alcohol0.187-0.111-0.0240.020-0.329-0.401-0.186-0.309-0.6990.1400.0051.0000.4470.147
quality0.100-0.098-0.2580.106-0.017-0.2950.087-0.055-0.3230.0330.0300.4471.0000.130
is red or white wine0.5520.5040.6640.4240.3500.7650.4190.8000.3220.3330.4720.1470.1301.000

Missing values

2023-11-21T16:50:54.103817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-21T16:50:54.257567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualityis red or white wine
007.40.700.001.90.07611.034.00.99783.510.569.451
117.80.880.002.60.09825.067.00.99683.200.689.851
227.80.760.042.30.09215.054.00.99703.260.659.851
3311.20.280.561.90.07517.060.00.99803.160.589.861
447.40.700.001.90.07611.034.00.99783.510.569.451
557.40.660.001.80.07513.040.00.99783.510.569.451
667.90.600.061.60.06915.059.00.99643.300.469.451
777.30.650.001.20.06515.021.00.99463.390.4710.071
887.80.580.022.00.0739.018.00.99683.360.579.571
997.50.500.366.10.07117.0102.00.99783.350.8010.551
Unnamed: 0fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualityis red or white wine
648748886.80.2200.361.200.05238.0127.00.993303.040.549.250
648848894.90.2350.2711.750.03034.0118.00.995403.070.509.460
648948906.10.3400.292.200.03625.0100.00.989383.060.4411.860
649048915.70.2100.320.900.03838.0121.00.990743.240.4610.660
649148926.50.2300.381.300.03229.0112.00.992983.290.549.750
649248936.20.2100.291.600.03924.092.00.991143.270.5011.260
649348946.60.3200.368.000.04757.0168.00.994903.150.469.650
649448956.50.2400.191.200.04130.0111.00.992542.990.469.460
649548965.50.2900.301.100.02220.0110.00.988693.340.3812.870
649648976.00.2100.380.800.02022.098.00.989413.260.3211.860